Overview

Dataset statistics

Number of variables18
Number of observations242
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory93.5 KiB
Average record size in memory395.5 B

Variable types

Categorical4
Numeric14

Alerts

Calories is highly overall correlated with Total Fat (g) and 5 other fieldsHigh correlation
Total Fat (g) is highly overall correlated with Calories and 4 other fieldsHigh correlation
Trans Fat (g) is highly overall correlated with Calories and 6 other fieldsHigh correlation
Sodium (mg) is highly overall correlated with Trans Fat (g) and 3 other fieldsHigh correlation
Total Carbohydrates (g) is highly overall correlated with Calories and 3 other fieldsHigh correlation
Cholesterol (mg) is highly overall correlated with Calories and 2 other fieldsHigh correlation
Dietary Fibre (g) is highly overall correlated with Iron (% DV) and 1 other fieldsHigh correlation
Sugars (g) is highly overall correlated with Calories and 2 other fieldsHigh correlation
Protein (g) is highly overall correlated with Calories and 5 other fieldsHigh correlation
Vitamin A (% DV) is highly overall correlated with Sodium (mg) and 2 other fieldsHigh correlation
Vitamin C (% DV) is highly overall correlated with BeverageHigh correlation
Calcium (% DV) is highly overall correlated with Total Fat (g) and 3 other fieldsHigh correlation
Iron (% DV) is highly overall correlated with Total Fat (g) and 1 other fieldsHigh correlation
Beverage_category is highly overall correlated with BeverageHigh correlation
Beverage is highly overall correlated with Dietary Fibre (g) and 2 other fieldsHigh correlation
Beverage_prep is highly overall correlated with Saturated Fat (g)High correlation
Saturated Fat (g) is highly overall correlated with Trans Fat (g) and 2 other fieldsHigh correlation
Calories has 4 (1.7%) zerosZeros
Total Fat (g) has 21 (8.7%) zerosZeros
Trans Fat (g) has 33 (13.6%) zerosZeros
Sodium (mg) has 112 (46.3%) zerosZeros
Total Carbohydrates (g) has 11 (4.5%) zerosZeros
Cholesterol (mg) has 8 (3.3%) zerosZeros
Dietary Fibre (g) has 141 (58.3%) zerosZeros
Sugars (g) has 14 (5.8%) zerosZeros
Protein (g) has 11 (4.5%) zerosZeros
Vitamin A (% DV) has 27 (11.2%) zerosZeros
Vitamin C (% DV) has 188 (77.7%) zerosZeros
Calcium (% DV) has 23 (9.5%) zerosZeros
Iron (% DV) has 108 (44.6%) zerosZeros
Caffeine (mg) has 35 (14.5%) zerosZeros

Reproduction

Analysis started2023-07-03 03:55:08.904172
Analysis finished2023-07-03 03:56:00.378922
Duration51.47 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

Distinct9
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size24.2 KiB
Classic Espresso Drinks
58 
Tazo® Tea Drinks
52 
Signature Espresso Drinks
40 
Frappuccino® Blended Coffee
36 
Shaken Iced Beverages
18 
Other values (4)
38 

Length

Max length33
Median length26
Mean length22.128099
Min length6

Characters and Unicode

Total characters5355
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCoffee
2nd rowCoffee
3rd rowCoffee
4th rowCoffee
5th rowClassic Espresso Drinks

Common Values

ValueCountFrequency (%)
Classic Espresso Drinks 58
24.0%
Tazo® Tea Drinks 52
21.5%
Signature Espresso Drinks 40
16.5%
Frappuccino® Blended Coffee 36
14.9%
Shaken Iced Beverages 18
 
7.4%
Frappuccino® Blended Crème 13
 
5.4%
Frappuccino® Light Blended Coffee 12
 
5.0%
Smoothies 9
 
3.7%
Coffee 4
 
1.7%

Length

2023-07-02T22:56:00.584448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-02T22:56:00.926565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
drinks 150
21.1%
espresso 98
13.8%
frappuccino® 61
8.6%
blended 61
8.6%
classic 58
 
8.1%
tazo® 52
 
7.3%
tea 52
 
7.3%
coffee 52
 
7.3%
signature 40
 
5.6%
shaken 18
 
2.5%
Other values (5) 70
9.8%

Most occurring characters

ValueCountFrequency (%)
s 587
 
11.0%
e 528
 
9.9%
470
 
8.8%
r 380
 
7.1%
n 330
 
6.2%
i 330
 
6.2%
a 299
 
5.6%
o 281
 
5.2%
p 220
 
4.1%
c 198
 
3.7%
Other values (22) 1732
32.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4060
75.8%
Uppercase Letter 712
 
13.3%
Space Separator 470
 
8.8%
Other Symbol 113
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 587
14.5%
e 528
13.0%
r 380
9.4%
n 330
8.1%
i 330
8.1%
a 299
7.4%
o 281
 
6.9%
p 220
 
5.4%
c 198
 
4.9%
k 168
 
4.1%
Other values (11) 739
18.2%
Uppercase Letter
ValueCountFrequency (%)
D 150
21.1%
C 123
17.3%
T 104
14.6%
E 98
13.8%
B 79
11.1%
S 67
9.4%
F 61
8.6%
I 18
 
2.5%
L 12
 
1.7%
Space Separator
ValueCountFrequency (%)
470
100.0%
Other Symbol
ValueCountFrequency (%)
® 113
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4772
89.1%
Common 583
 
10.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 587
12.3%
e 528
 
11.1%
r 380
 
8.0%
n 330
 
6.9%
i 330
 
6.9%
a 299
 
6.3%
o 281
 
5.9%
p 220
 
4.6%
c 198
 
4.1%
k 168
 
3.5%
Other values (20) 1451
30.4%
Common
ValueCountFrequency (%)
470
80.6%
® 113
 
19.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5229
97.6%
None 126
 
2.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 587
 
11.2%
e 528
 
10.1%
470
 
9.0%
r 380
 
7.3%
n 330
 
6.3%
i 330
 
6.3%
a 299
 
5.7%
o 281
 
5.4%
p 220
 
4.2%
c 198
 
3.8%
Other values (20) 1606
30.7%
None
ValueCountFrequency (%)
® 113
89.7%
è 13
 
10.3%

Beverage
Categorical

Distinct33
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Memory size25.2 KiB
Tazo® Full-Leaf Red Tea Latte (Vanilla Rooibos)
 
12
White Chocolate Mocha (Without Whipped Cream)
 
12
Tazo® Full-Leaf Tea Latte
 
12
Tazo® Green Tea Latte
 
12
Tazo® Chai Tea Latte
 
12
Other values (28)
182 

Length

Max length51
Median length37
Mean length27.665289
Min length5

Characters and Unicode

Total characters6695
Distinct characters47
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBrewed Coffee
2nd rowBrewed Coffee
3rd rowBrewed Coffee
4th rowBrewed Coffee
5th rowCaffè Latte

Common Values

ValueCountFrequency (%)
Tazo® Full-Leaf Red Tea Latte (Vanilla Rooibos) 12
 
5.0%
White Chocolate Mocha (Without Whipped Cream) 12
 
5.0%
Tazo® Full-Leaf Tea Latte 12
 
5.0%
Tazo® Green Tea Latte 12
 
5.0%
Tazo® Chai Tea Latte 12
 
5.0%
Coffee 12
 
5.0%
Hot Chocolate (Without Whipped Cream) 12
 
5.0%
Caramel Macchiato 12
 
5.0%
Cappuccino 12
 
5.0%
Vanilla Latte (Or Other Flavoured Latte) 12
 
5.0%
Other values (23) 122
50.4%

Length

2023-07-02T22:56:01.264530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
latte 88
 
8.8%
without 80
 
8.0%
cream 80
 
8.0%
whipped 80
 
8.0%
tazo® 58
 
5.8%
tea 58
 
5.8%
mocha 36
 
3.6%
caffè 28
 
2.8%
vanilla 28
 
2.8%
coffee 28
 
2.8%
Other values (40) 434
43.5%

Most occurring characters

ValueCountFrequency (%)
756
 
11.3%
a 679
 
10.1%
e 635
 
9.5%
t 450
 
6.7%
o 374
 
5.6%
i 335
 
5.0%
h 316
 
4.7%
C 254
 
3.8%
r 248
 
3.7%
p 228
 
3.4%
Other values (37) 2420
36.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4583
68.5%
Uppercase Letter 1004
 
15.0%
Space Separator 756
 
11.3%
Open Punctuation 126
 
1.9%
Close Punctuation 126
 
1.9%
Other Symbol 58
 
0.9%
Dash Punctuation 24
 
0.4%
Other Punctuation 18
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 679
14.8%
e 635
13.9%
t 450
9.8%
o 374
 
8.2%
i 335
 
7.3%
h 316
 
6.9%
r 248
 
5.4%
p 228
 
5.0%
l 206
 
4.5%
c 155
 
3.4%
Other values (14) 957
20.9%
Uppercase Letter
ValueCountFrequency (%)
C 254
25.3%
W 190
18.9%
T 116
11.6%
L 115
11.5%
M 60
 
6.0%
S 53
 
5.3%
F 40
 
4.0%
B 29
 
2.9%
V 28
 
2.8%
O 27
 
2.7%
Other values (7) 92
 
9.2%
Space Separator
ValueCountFrequency (%)
756
100.0%
Open Punctuation
ValueCountFrequency (%)
( 126
100.0%
Close Punctuation
ValueCountFrequency (%)
) 126
100.0%
Other Symbol
ValueCountFrequency (%)
® 58
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 24
100.0%
Other Punctuation
ValueCountFrequency (%)
& 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5587
83.5%
Common 1108
 
16.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 679
 
12.2%
e 635
 
11.4%
t 450
 
8.1%
o 374
 
6.7%
i 335
 
6.0%
h 316
 
5.7%
C 254
 
4.5%
r 248
 
4.4%
p 228
 
4.1%
l 206
 
3.7%
Other values (31) 1862
33.3%
Common
ValueCountFrequency (%)
756
68.2%
( 126
 
11.4%
) 126
 
11.4%
® 58
 
5.2%
- 24
 
2.2%
& 18
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6600
98.6%
None 95
 
1.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
756
 
11.5%
a 679
 
10.3%
e 635
 
9.6%
t 450
 
6.8%
o 374
 
5.7%
i 335
 
5.1%
h 316
 
4.8%
C 254
 
3.8%
r 248
 
3.8%
p 228
 
3.5%
Other values (35) 2325
35.2%
None
ValueCountFrequency (%)
® 58
61.1%
è 37
38.9%

Beverage_prep
Categorical

Distinct13
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size16.0 KiB
Soymilk
66 
2 Milk
50 
Grande Nonfat Milk
26 
Tall Nonfat Milk
23 
Venti Nonfat Milk
22 
Other values (8)
55 

Length

Max length18
Median length17
Mean length10.210744
Min length4

Characters and Unicode

Total characters2471
Distinct characters25
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.8%

Sample

1st rowShort
2nd rowTall
3rd rowGrande
4th rowVenti
5th rowShort Nonfat Milk

Common Values

ValueCountFrequency (%)
Soymilk 66
27.3%
2 Milk 50
20.7%
Grande Nonfat Milk 26
 
10.7%
Tall Nonfat Milk 23
 
9.5%
Venti Nonfat Milk 22
 
9.1%
Whole Milk 16
 
6.6%
Short Nonfat Milk 12
 
5.0%
Tall 7
 
2.9%
Grande 7
 
2.9%
Venti 7
 
2.9%
Other values (3) 6
 
2.5%

Length

2023-07-02T22:56:01.515857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
milk 149
31.4%
nonfat 83
17.5%
soymilk 66
13.9%
2 50
 
10.5%
grande 33
 
7.0%
tall 30
 
6.3%
venti 29
 
6.1%
whole 16
 
3.4%
short 16
 
3.4%
solo 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
l 292
11.8%
i 245
 
9.9%
232
 
9.4%
k 215
 
8.7%
o 185
 
7.5%
M 149
 
6.0%
a 146
 
5.9%
n 145
 
5.9%
t 128
 
5.2%
f 83
 
3.4%
Other values (15) 651
26.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1765
71.4%
Uppercase Letter 424
 
17.2%
Space Separator 232
 
9.4%
Decimal Number 50
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 292
16.5%
i 245
13.9%
k 215
12.2%
o 185
10.5%
a 146
8.3%
n 145
8.2%
t 128
7.3%
f 83
 
4.7%
e 78
 
4.4%
m 66
 
3.7%
Other values (5) 182
10.3%
Uppercase Letter
ValueCountFrequency (%)
M 149
35.1%
N 83
19.6%
S 83
19.6%
G 33
 
7.8%
T 30
 
7.1%
V 29
 
6.8%
W 16
 
3.8%
D 1
 
0.2%
Space Separator
ValueCountFrequency (%)
232
100.0%
Decimal Number
ValueCountFrequency (%)
2 50
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2189
88.6%
Common 282
 
11.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 292
13.3%
i 245
11.2%
k 215
9.8%
o 185
 
8.5%
M 149
 
6.8%
a 146
 
6.7%
n 145
 
6.6%
t 128
 
5.8%
f 83
 
3.8%
N 83
 
3.8%
Other values (13) 518
23.7%
Common
ValueCountFrequency (%)
232
82.3%
2 50
 
17.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2471
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 292
11.8%
i 245
 
9.9%
232
 
9.4%
k 215
 
8.7%
o 185
 
7.5%
M 149
 
6.0%
a 146
 
5.9%
n 145
 
5.9%
t 128
 
5.2%
f 83
 
3.4%
Other values (15) 651
26.3%

Calories
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct48
Distinct (%)19.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean193.8719
Minimum0
Maximum510
Zeros4
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-07-02T22:56:01.773167image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15.5
Q1120
median185
Q3260
95-th percentile370
Maximum510
Range510
Interquartile range (IQR)140

Descriptive statistics

Standard deviation102.8633
Coefficient of variation (CV)0.53057355
Kurtosis-0.082196546
Mean193.8719
Median Absolute Deviation (MAD)75
Skewness0.3783959
Sum46917
Variance10580.859
MonotonicityNot monotonic
2023-07-02T22:56:02.048254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
150 11
 
4.5%
190 11
 
4.5%
180 11
 
4.5%
120 10
 
4.1%
100 10
 
4.1%
130 10
 
4.1%
200 10
 
4.1%
240 9
 
3.7%
110 9
 
3.7%
170 9
 
3.7%
Other values (38) 142
58.7%
ValueCountFrequency (%)
0 4
1.7%
3 1
 
0.4%
4 1
 
0.4%
5 4
1.7%
10 2
0.8%
15 1
 
0.4%
25 1
 
0.4%
50 2
0.8%
60 4
1.7%
70 3
1.2%
ValueCountFrequency (%)
510 1
 
0.4%
460 2
0.8%
450 2
0.8%
430 1
 
0.4%
420 1
 
0.4%
400 1
 
0.4%
390 2
0.8%
380 1
 
0.4%
370 3
1.2%
360 1
 
0.4%

Total Fat (g)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9049587
Minimum0
Maximum15
Zeros21
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-07-02T22:56:02.284316image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.2
median2.5
Q34.5
95-th percentile9
Maximum15
Range15
Interquartile range (IQR)4.3

Descriptive statistics

Standard deviation2.9443765
Coefficient of variation (CV)1.0135691
Kurtosis1.1534569
Mean2.9049587
Median Absolute Deviation (MAD)2.3
Skewness1.1478455
Sum703
Variance8.6693529
MonotonicityNot monotonic
2023-07-02T22:56:02.484780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0.1 34
14.0%
0 21
 
8.7%
1.5 16
 
6.6%
5 15
 
6.2%
3 15
 
6.2%
4 14
 
5.8%
0.2 14
 
5.8%
1 13
 
5.4%
2.5 13
 
5.4%
6 13
 
5.4%
Other values (14) 74
30.6%
ValueCountFrequency (%)
0 21
8.7%
0.1 34
14.0%
0.2 14
5.8%
0.3 6
 
2.5%
0.4 2
 
0.8%
0.5 4
 
1.7%
1 13
 
5.4%
1.5 16
6.6%
2 10
 
4.1%
2.5 13
 
5.4%
ValueCountFrequency (%)
15 1
 
0.4%
13 1
 
0.4%
11 3
 
1.2%
10 3
 
1.2%
9 6
 
2.5%
8 6
 
2.5%
7 10
4.1%
6 13
5.4%
5 15
6.2%
4.5 9
3.7%

Trans Fat (g)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3070248
Minimum0
Maximum9
Zeros33
Zeros (%)13.6%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-07-02T22:56:02.703866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1
median0.5
Q32
95-th percentile4.5
Maximum9
Range9
Interquartile range (IQR)1.9

Descriptive statistics

Standard deviation1.6402586
Coefficient of variation (CV)1.254956
Kurtosis2.9263019
Mean1.3070248
Median Absolute Deviation (MAD)0.5
Skewness1.6948251
Sum316.3
Variance2.6904484
MonotonicityNot monotonic
2023-07-02T22:56:02.898345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0.1 36
14.9%
0 33
13.6%
0.2 22
9.1%
1 21
8.7%
2 20
8.3%
0.5 20
8.3%
1.5 16
 
6.6%
0.4 12
 
5.0%
3.5 11
 
4.5%
2.5 10
 
4.1%
Other values (8) 41
16.9%
ValueCountFrequency (%)
0 33
13.6%
0.1 36
14.9%
0.2 22
9.1%
0.3 10
 
4.1%
0.4 12
 
5.0%
0.5 20
8.3%
1 21
8.7%
1.5 16
6.6%
2 20
8.3%
2.5 10
 
4.1%
ValueCountFrequency (%)
9 1
 
0.4%
7 2
 
0.8%
6 5
 
2.1%
5 4
 
1.7%
4.5 6
 
2.5%
4 3
 
1.2%
3.5 11
4.5%
3 10
4.1%
2.5 10
4.1%
2 20
8.3%
Distinct4
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
0.0
180 
0.1
37 
0.2
21 
0.3
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters726
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 180
74.4%
0.1 37
 
15.3%
0.2 21
 
8.7%
0.3 4
 
1.7%

Length

2023-07-02T22:56:03.096018image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-02T22:56:03.304456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 180
74.4%
0.1 37
 
15.3%
0.2 21
 
8.7%
0.3 4
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 422
58.1%
. 242
33.3%
1 37
 
5.1%
2 21
 
2.9%
3 4
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 484
66.7%
Other Punctuation 242
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 422
87.2%
1 37
 
7.6%
2 21
 
4.3%
3 4
 
0.8%
Other Punctuation
ValueCountFrequency (%)
. 242
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 726
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 422
58.1%
. 242
33.3%
1 37
 
5.1%
2 21
 
2.9%
3 4
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 726
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 422
58.1%
. 242
33.3%
1 37
 
5.1%
2 21
 
2.9%
3 4
 
0.6%

Sodium (mg)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3636364
Minimum0
Maximum40
Zeros112
Zeros (%)46.3%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-07-02T22:56:03.484975image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q310
95-th percentile25
Maximum40
Range40
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.6302565
Coefficient of variation (CV)1.3561832
Kurtosis2.5107137
Mean6.3636364
Median Absolute Deviation (MAD)5
Skewness1.6806599
Sum1540
Variance74.481328
MonotonicityNot monotonic
2023-07-02T22:56:03.665524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 112
46.3%
5 57
23.6%
10 28
 
11.6%
15 19
 
7.9%
25 9
 
3.7%
20 8
 
3.3%
35 5
 
2.1%
30 3
 
1.2%
40 1
 
0.4%
ValueCountFrequency (%)
0 112
46.3%
5 57
23.6%
10 28
 
11.6%
15 19
 
7.9%
20 8
 
3.3%
25 9
 
3.7%
30 3
 
1.2%
35 5
 
2.1%
40 1
 
0.4%
ValueCountFrequency (%)
40 1
 
0.4%
35 5
 
2.1%
30 3
 
1.2%
25 9
 
3.7%
20 8
 
3.3%
15 19
 
7.9%
10 28
 
11.6%
5 57
23.6%
0 112
46.3%

Total Carbohydrates (g)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct51
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.8843
Minimum0
Maximum340
Zeros11
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-07-02T22:56:03.917820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.05
Q170
median125
Q3170
95-th percentile290
Maximum340
Range340
Interquartile range (IQR)100

Descriptive statistics

Standard deviation82.303223
Coefficient of variation (CV)0.63858224
Kurtosis-0.26034258
Mean128.8843
Median Absolute Deviation (MAD)55
Skewness0.47784293
Sum31190
Variance6773.8206
MonotonicityNot monotonic
2023-07-02T22:56:04.189751image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
160 16
 
6.6%
125 11
 
4.5%
150 11
 
4.5%
0 11
 
4.5%
140 10
 
4.1%
220 9
 
3.7%
80 9
 
3.7%
120 8
 
3.3%
170 8
 
3.3%
70 8
 
3.3%
Other values (41) 141
58.3%
ValueCountFrequency (%)
0 11
4.5%
1 1
 
0.4%
4 1
 
0.4%
5 4
 
1.7%
10 5
2.1%
15 3
 
1.2%
20 2
 
0.8%
25 3
 
1.2%
30 2
 
0.8%
35 1
 
0.4%
ValueCountFrequency (%)
340 2
 
0.8%
330 2
 
0.8%
320 1
 
0.4%
310 1
 
0.4%
300 6
2.5%
290 4
1.7%
280 1
 
0.4%
270 2
 
0.8%
260 2
 
0.8%
250 4
1.7%

Cholesterol (mg)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct75
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.991736
Minimum0
Maximum90
Zeros8
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-07-02T22:56:04.481970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.05
Q121
median34
Q350.75
95-th percentile72.95
Maximum90
Range90
Interquartile range (IQR)29.75

Descriptive statistics

Standard deviation20.795186
Coefficient of variation (CV)0.5777767
Kurtosis-0.3840356
Mean35.991736
Median Absolute Deviation (MAD)15
Skewness0.38901299
Sum8710
Variance432.43977
MonotonicityNot monotonic
2023-07-02T22:56:04.739938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 10
 
4.1%
0 8
 
3.3%
23 8
 
3.3%
53 8
 
3.3%
42 7
 
2.9%
34 7
 
2.9%
16 7
 
2.9%
21 7
 
2.9%
37 7
 
2.9%
70 6
 
2.5%
Other values (65) 167
69.0%
ValueCountFrequency (%)
0 8
3.3%
1 2
 
0.8%
2 2
 
0.8%
3 1
 
0.4%
4 2
 
0.8%
6 1
 
0.4%
7 1
 
0.4%
8 2
 
0.8%
9 4
1.7%
10 3
 
1.2%
ValueCountFrequency (%)
90 2
 
0.8%
89 1
 
0.4%
88 1
 
0.4%
80 2
 
0.8%
78 4
1.7%
77 1
 
0.4%
75 1
 
0.4%
73 1
 
0.4%
72 1
 
0.4%
70 6
2.5%

Dietary Fibre (g)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.80578512
Minimum0
Maximum8
Zeros141
Zeros (%)58.3%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-07-02T22:56:04.956360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4459443
Coefficient of variation (CV)1.7944539
Kurtosis9.3590619
Mean0.80578512
Median Absolute Deviation (MAD)0
Skewness2.8930215
Sum195
Variance2.0907548
MonotonicityNot monotonic
2023-07-02T22:56:05.137751image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 141
58.3%
1 60
24.8%
2 25
 
10.3%
7 5
 
2.1%
3 4
 
1.7%
4 3
 
1.2%
6 3
 
1.2%
8 1
 
0.4%
ValueCountFrequency (%)
0 141
58.3%
1 60
24.8%
2 25
 
10.3%
3 4
 
1.7%
4 3
 
1.2%
6 3
 
1.2%
7 5
 
2.1%
8 1
 
0.4%
ValueCountFrequency (%)
8 1
 
0.4%
7 5
 
2.1%
6 3
 
1.2%
4 3
 
1.2%
3 4
 
1.7%
2 25
 
10.3%
1 60
24.8%
0 141
58.3%

Sugars (g)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct70
Distinct (%)28.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.96281
Minimum0
Maximum84
Zeros14
Zeros (%)5.8%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-07-02T22:56:05.392113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q118
median32
Q343.75
95-th percentile71
Maximum84
Range84
Interquartile range (IQR)25.75

Descriptive statistics

Standard deviation19.730199
Coefficient of variation (CV)0.59855939
Kurtosis-0.20968454
Mean32.96281
Median Absolute Deviation (MAD)12
Skewness0.46815548
Sum7977
Variance389.28077
MonotonicityNot monotonic
2023-07-02T22:56:05.666380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14
 
5.8%
32 10
 
4.1%
23 9
 
3.7%
41 8
 
3.3%
33 7
 
2.9%
38 6
 
2.5%
18 6
 
2.5%
17 6
 
2.5%
25 6
 
2.5%
31 6
 
2.5%
Other values (60) 164
67.8%
ValueCountFrequency (%)
0 14
5.8%
3 1
 
0.4%
4 2
 
0.8%
5 1
 
0.4%
6 1
 
0.4%
7 3
 
1.2%
8 4
 
1.7%
9 2
 
0.8%
10 2
 
0.8%
11 2
 
0.8%
ValueCountFrequency (%)
84 2
0.8%
83 1
 
0.4%
80 1
 
0.4%
77 2
0.8%
76 2
0.8%
74 2
0.8%
73 2
0.8%
71 2
0.8%
69 3
1.2%
68 1
 
0.4%

Protein (g)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9785124
Minimum0
Maximum20
Zeros11
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-07-02T22:56:06.273413image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q13
median6
Q310
95-th percentile16
Maximum20
Range20
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.8716592
Coefficient of variation (CV)0.69809422
Kurtosis-0.22941771
Mean6.9785124
Median Absolute Deviation (MAD)3
Skewness0.70746604
Sum1688.8
Variance23.733063
MonotonicityNot monotonic
2023-07-02T22:56:06.539886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
3 30
12.4%
6 25
 
10.3%
5 23
 
9.5%
4 22
 
9.1%
7 20
 
8.3%
10 13
 
5.4%
9 13
 
5.4%
0 11
 
4.5%
16 8
 
3.3%
2 8
 
3.3%
Other values (16) 69
28.5%
ValueCountFrequency (%)
0 11
 
4.5%
0.1 3
 
1.2%
0.2 1
 
0.4%
0.3 2
 
0.8%
0.4 3
 
1.2%
0.5 1
 
0.4%
1 6
 
2.5%
2 8
 
3.3%
3 30
12.4%
4 22
9.1%
ValueCountFrequency (%)
20 2
 
0.8%
19 3
 
1.2%
18 2
 
0.8%
17 4
1.7%
16 8
3.3%
15 7
2.9%
14 6
2.5%
13 7
2.9%
12 7
2.9%
11 7
2.9%

Vitamin A (% DV)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8305785
Minimum0
Maximum50
Zeros27
Zeros (%)11.2%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-07-02T22:56:06.808903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median8
Q315
95-th percentile25
Maximum50
Range50
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.0979619
Coefficient of variation (CV)0.82375232
Kurtosis6.1269688
Mean9.8305785
Median Absolute Deviation (MAD)4
Skewness1.8558885
Sum2379
Variance65.576986
MonotonicityNot monotonic
2023-07-02T22:56:07.021332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
10 43
17.8%
6 37
15.3%
4 37
15.3%
15 36
14.9%
0 27
11.2%
8 23
9.5%
20 18
7.4%
25 11
 
4.5%
2 5
 
2.1%
50 3
 
1.2%
ValueCountFrequency (%)
0 27
11.2%
2 5
 
2.1%
4 37
15.3%
6 37
15.3%
8 23
9.5%
10 43
17.8%
15 36
14.9%
20 18
7.4%
25 11
 
4.5%
30 2
 
0.8%
ValueCountFrequency (%)
50 3
 
1.2%
30 2
 
0.8%
25 11
 
4.5%
20 18
7.4%
15 36
14.9%
10 43
17.8%
8 23
9.5%
6 37
15.3%
4 37
15.3%
2 5
 
2.1%

Vitamin C (% DV)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6487603
Minimum0
Maximum100
Zeros188
Zeros (%)77.7%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-07-02T22:56:07.247736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile15
Maximum100
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation14.421794
Coefficient of variation (CV)3.9525189
Kurtosis32.456801
Mean3.6487603
Median Absolute Deviation (MAD)0
Skewness5.6332425
Sum883
Variance207.98815
MonotonicityNot monotonic
2023-07-02T22:56:07.426292image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 188
77.7%
2 20
 
8.3%
6 7
 
2.9%
15 7
 
2.9%
10 4
 
1.7%
20 4
 
1.7%
4 3
 
1.2%
80 3
 
1.2%
100 3
 
1.2%
8 3
 
1.2%
ValueCountFrequency (%)
0 188
77.7%
2 20
 
8.3%
4 3
 
1.2%
6 7
 
2.9%
8 3
 
1.2%
10 4
 
1.7%
15 7
 
2.9%
20 4
 
1.7%
80 3
 
1.2%
100 3
 
1.2%
ValueCountFrequency (%)
100 3
 
1.2%
80 3
 
1.2%
20 4
 
1.7%
15 7
 
2.9%
10 4
 
1.7%
8 3
 
1.2%
6 7
 
2.9%
4 3
 
1.2%
2 20
 
8.3%
0 188
77.7%

Calcium (% DV)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.756198
Minimum0
Maximum60
Zeros23
Zeros (%)9.5%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-07-02T22:56:07.643682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median20
Q330
95-th percentile50
Maximum60
Range60
Interquartile range (IQR)20

Descriptive statistics

Standard deviation14.542343
Coefficient of variation (CV)0.70062651
Kurtosis-0.16619751
Mean20.756198
Median Absolute Deviation (MAD)10
Skewness0.66258986
Sum5023
Variance211.47973
MonotonicityNot monotonic
2023-07-02T22:56:07.830182image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
10 51
21.1%
20 35
14.5%
15 24
9.9%
0 23
9.5%
30 21
8.7%
25 21
8.7%
35 17
 
7.0%
45 11
 
4.5%
40 9
 
3.7%
50 9
 
3.7%
Other values (4) 21
8.7%
ValueCountFrequency (%)
0 23
9.5%
2 4
 
1.7%
6 3
 
1.2%
8 9
 
3.7%
10 51
21.1%
15 24
9.9%
20 35
14.5%
25 21
8.7%
30 21
8.7%
35 17
 
7.0%
ValueCountFrequency (%)
60 5
 
2.1%
50 9
 
3.7%
45 11
 
4.5%
40 9
 
3.7%
35 17
 
7.0%
30 21
8.7%
25 21
8.7%
20 35
14.5%
15 24
9.9%
10 51
21.1%

Iron (% DV)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.446281
Minimum0
Maximum50
Zeros108
Zeros (%)44.6%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-07-02T22:56:08.027650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q310
95-th percentile30
Maximum50
Range50
Interquartile range (IQR)10

Descriptive statistics

Standard deviation10.486467
Coefficient of variation (CV)1.4082824
Kurtosis2.4600691
Mean7.446281
Median Absolute Deviation (MAD)2
Skewness1.6730341
Sum1802
Variance109.96598
MonotonicityNot monotonic
2023-07-02T22:56:08.222548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 108
44.6%
2 20
 
8.3%
10 18
 
7.4%
8 16
 
6.6%
20 16
 
6.6%
6 15
 
6.2%
15 12
 
5.0%
4 11
 
4.5%
25 9
 
3.7%
30 9
 
3.7%
Other values (3) 8
 
3.3%
ValueCountFrequency (%)
0 108
44.6%
2 20
 
8.3%
4 11
 
4.5%
6 15
 
6.2%
8 16
 
6.6%
10 18
 
7.4%
15 12
 
5.0%
20 16
 
6.6%
25 9
 
3.7%
30 9
 
3.7%
ValueCountFrequency (%)
50 2
 
0.8%
40 3
 
1.2%
35 3
 
1.2%
30 9
3.7%
25 9
3.7%
20 16
6.6%
15 12
5.0%
10 18
7.4%
8 16
6.6%
6 15
6.2%

Caffeine (mg)
Real number (ℝ)

Distinct35
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.520548
Minimum0
Maximum410
Zeros35
Zeros (%)14.5%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-07-02T22:56:08.456925image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q170
median89.520548
Q3130
95-th percentile174.75
Maximum410
Range410
Interquartile range (IQR)60

Descriptive statistics

Standard deviation61.560774
Coefficient of variation (CV)0.68767199
Kurtosis3.2746549
Mean89.520548
Median Absolute Deviation (MAD)37.5
Skewness0.92235124
Sum21663.973
Variance3789.7289
MonotonicityNot monotonic
2023-07-02T22:56:08.686996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
75 37
15.3%
0 35
14.5%
150 34
14.0%
89.52054795 23
 
9.5%
70 14
 
5.8%
95 11
 
4.5%
110 9
 
3.7%
130 7
 
2.9%
120 6
 
2.5%
25 6
 
2.5%
Other values (25) 60
24.8%
ValueCountFrequency (%)
0 35
14.5%
10 3
 
1.2%
15 3
 
1.2%
20 3
 
1.2%
25 6
 
2.5%
30 3
 
1.2%
50 3
 
1.2%
55 3
 
1.2%
65 1
 
0.4%
70 14
 
5.8%
ValueCountFrequency (%)
410 1
 
0.4%
330 1
 
0.4%
300 1
 
0.4%
260 1
 
0.4%
235 1
 
0.4%
225 1
 
0.4%
180 3
1.2%
175 4
1.7%
170 3
1.2%
165 2
0.8%

Interactions

2023-07-02T22:55:56.002299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:11.230703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:14.627691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:18.002894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:21.360641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:24.838343image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:28.567098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:31.855064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:35.409072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:38.831589image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:42.413222image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:45.792249image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:49.072421image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:52.701836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:56.305919image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:11.635351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:14.870010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:18.228259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:21.606884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:25.082688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:28.793178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:32.107698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:35.629482image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:39.092679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:42.632635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:46.014352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:49.282858image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:52.946120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:56.548883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:11.852369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:15.116351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:18.619787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:21.859626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:25.341062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:29.041569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:32.358639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:35.877746image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:39.358996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:42.902562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:46.262998image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:49.527206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:53.179493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:56.750341image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:12.062943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:15.340644image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:18.826236image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:22.092004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:25.589396image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:29.256999image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:32.589018image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:36.121098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:39.596328image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:43.133943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:46.483746image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:49.739640image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:53.391930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:57.005703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:12.320635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:15.593773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:19.070582image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:22.350979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:25.847695image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:29.491366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:32.862221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:36.361548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:39.869483image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:43.397900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:46.736104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:50.003718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:53.638267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:57.243071image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2023-07-02T22:55:15.850081image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:19.307950image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:22.616226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:26.123959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:29.730480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:33.113551image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:36.599877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:40.129788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:43.684133image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:46.979676image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:50.261646image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:53.889597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:57.462482image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:12.797642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:16.078470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:19.513430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:22.849530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:26.385209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:29.952888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:33.336953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:36.810870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:40.370866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:43.939183image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:47.208096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:50.481775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:54.116737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:57.727732image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:13.049968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:16.360071image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:19.779414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:23.114820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:26.653426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:30.223985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:33.595261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:37.043248image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:40.637750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:44.195557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:47.462412image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:50.729083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:54.366040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:57.943159image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:13.273371image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:16.582136image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:19.977885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:23.353184image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:26.897355image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:30.469243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:33.829654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:37.254683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:40.885792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:44.408751image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:47.660919image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:50.944942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:54.577137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:58.190316image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:13.518199image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:16.841638image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:20.233886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:23.609970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:27.350178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:30.748190image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:34.088961image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:37.506011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:41.150084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:44.672014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:47.903760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:51.484502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:54.839436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:58.421696image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:13.736618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:17.078005image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:20.459929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:23.859301image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:27.596490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:30.967603image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:34.340321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:37.720437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:41.397423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:44.904082image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:48.122176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:51.707936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:55.076462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:58.632901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:13.966002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:17.310388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:20.686440image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:24.105676image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:27.840884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:31.193003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:34.583267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:38.174614image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:41.638777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:45.133429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:48.351563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:51.944228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:55.301523image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:58.886323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:14.203369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:17.549075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:20.929790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:24.358567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:28.089183image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:31.429369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:34.904207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:38.394751image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:41.894009image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:45.365494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:48.582713image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:52.188579image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:55.528915image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:59.166372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:14.425524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:17.784477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:21.159210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:24.603944image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:28.331724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:31.649781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:35.180682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:38.605188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:42.156309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:45.579840image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:48.844857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:52.437908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T22:55:55.743340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-07-02T22:56:08.924849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
CaloriesTotal Fat (g)Trans Fat (g)Sodium (mg)Total Carbohydrates (g)Cholesterol (mg)Dietary Fibre (g)Sugars (g)Protein (g)Vitamin A (% DV)Vitamin C (% DV)Calcium (% DV)Iron (% DV)Caffeine (mg)Beverage_categoryBeverageBeverage_prepSaturated Fat (g)
Calories1.0000.5860.6000.3440.7830.9450.4510.9100.5440.4180.4510.4580.4630.0550.2940.3470.2300.222
Total Fat (g)0.5861.0000.9290.4370.4740.3540.4960.3040.6030.4840.2720.6260.5240.1060.0650.0000.1690.494
Trans Fat (g)0.6000.9291.0000.6040.5310.4080.4140.3670.5860.4850.2550.5450.4200.0910.0860.0000.1570.539
Sodium (mg)0.3440.4370.6041.0000.3200.217-0.1410.2140.5310.5770.2670.441-0.1770.0420.0960.0000.3820.819
Total Carbohydrates (g)0.7830.4740.5310.3201.0000.7440.2870.7420.4440.3940.2570.4150.3430.1360.3550.4080.3280.247
Cholesterol (mg)0.9450.3540.4080.2170.7441.0000.3780.9830.3380.2270.3950.2310.405-0.0240.2970.4080.1730.000
Dietary Fibre (g)0.4510.4960.414-0.1410.2870.3781.0000.2630.4360.1940.2320.2890.869-0.0500.3610.5580.1300.000
Sugars (g)0.9100.3040.3670.2140.7420.9830.2631.0000.2510.1880.3520.1970.321-0.0200.3040.3370.1870.076
Protein (g)0.5440.6030.5860.5310.4440.3380.4360.2511.0000.8980.4060.8860.3490.1140.3720.4340.2790.360
Vitamin A (% DV)0.4180.4840.4850.5770.3940.2270.1940.1880.8981.0000.2640.9030.1710.1190.3210.4460.2970.428
Vitamin C (% DV)0.4510.2720.2550.2670.2570.3950.2320.3520.4060.2641.0000.2480.124-0.1690.4890.8720.0000.000
Calcium (% DV)0.4580.6260.5450.4410.4150.2310.2890.1970.8860.9030.2481.0000.2980.1690.3160.4000.3120.347
Iron (% DV)0.4630.5240.420-0.1770.3430.4050.8690.3210.3490.1710.1240.2981.000-0.0260.0620.2160.1790.077
Caffeine (mg)0.0550.1060.0910.0420.136-0.024-0.050-0.0200.1140.119-0.1690.169-0.0261.0000.4120.4710.2540.000
Beverage_category0.2940.0650.0860.0960.3550.2970.3610.3040.3720.3210.4890.3160.0620.4121.0000.9300.2490.000
Beverage0.3470.0000.0000.0000.4080.4080.5580.3370.4340.4460.8720.4000.2160.4710.9301.0000.3010.000
Beverage_prep0.2300.1690.1570.3820.3280.1730.1300.1870.2790.2970.0000.3120.1790.2540.2490.3011.0000.533
Saturated Fat (g)0.2220.4940.5390.8190.2470.0000.0000.0760.3600.4280.0000.3470.0770.0000.0000.0000.5331.000

Missing values

2023-07-02T22:55:59.515439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-02T22:56:00.097825image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Beverage_categoryBeverageBeverage_prepCaloriesTotal Fat (g)Trans Fat (g)Saturated Fat (g)Sodium (mg)Total Carbohydrates (g)Cholesterol (mg)Dietary Fibre (g)Sugars (g)Protein (g)Vitamin A (% DV)Vitamin C (% DV)Calcium (% DV)Iron (% DV)Caffeine (mg)
0CoffeeBrewed CoffeeShort3.00.10.00.00.05.00.00.00.00.30.00.00.00.0175.0
1CoffeeBrewed CoffeeTall4.00.10.00.00.010.00.00.00.00.50.00.00.00.0260.0
2CoffeeBrewed CoffeeGrande5.00.10.00.00.010.00.00.00.01.00.00.00.00.0330.0
3CoffeeBrewed CoffeeVenti5.00.10.00.00.010.00.00.00.01.00.00.02.00.0410.0
4Classic Espresso DrinksCaffè LatteShort Nonfat Milk70.00.10.10.05.075.010.00.09.06.010.00.020.00.075.0
5Classic Espresso DrinksCaffè Latte2 Milk100.03.52.00.115.085.010.00.09.06.010.00.020.00.075.0
6Classic Espresso DrinksCaffè LatteSoymilk70.02.50.40.00.065.06.01.04.05.06.00.020.08.075.0
7Classic Espresso DrinksCaffè LatteTall Nonfat Milk100.00.20.20.05.0120.015.00.014.010.015.00.030.00.075.0
8Classic Espresso DrinksCaffè Latte2 Milk150.06.03.00.225.0135.015.00.014.010.015.00.030.00.075.0
9Classic Espresso DrinksCaffè LatteSoymilk110.04.50.50.00.0105.010.01.06.08.010.00.030.015.075.0
Beverage_categoryBeverageBeverage_prepCaloriesTotal Fat (g)Trans Fat (g)Saturated Fat (g)Sodium (mg)Total Carbohydrates (g)Cholesterol (mg)Dietary Fibre (g)Sugars (g)Protein (g)Vitamin A (% DV)Vitamin C (% DV)Calcium (% DV)Iron (% DV)Caffeine (mg)
232Frappuccino® Blended CrèmeStrawberries & Crème (Without Whipped Cream)Grande Nonfat Milk230.00.20.10.00.0190.053.00.052.04.08.06.015.04.00.0
233Frappuccino® Blended CrèmeStrawberries & Crème (Without Whipped Cream)Whole Milk260.04.02.00.110.0190.053.00.052.04.06.06.015.04.00.0
234Frappuccino® Blended CrèmeStrawberries & Crème (Without Whipped Cream)Soymilk240.02.00.20.00.0180.051.01.049.03.04.06.015.08.00.0
235Frappuccino® Blended CrèmeStrawberries & Crème (Without Whipped Cream)Venti Nonfat Milk310.00.20.10.05.0260.070.00.069.06.010.08.020.04.00.0
236Frappuccino® Blended CrèmeStrawberries & Crème (Without Whipped Cream)Whole Milk350.06.03.00.215.0260.070.00.068.06.08.08.020.04.00.0
237Frappuccino® Blended CrèmeStrawberries & Crème (Without Whipped Cream)Soymilk320.03.20.40.00.0250.067.01.064.05.06.08.020.010.00.0
238Frappuccino® Blended CrèmeVanilla Bean (Without Whipped Cream)Tall Nonfat Milk170.00.10.10.00.0160.039.00.038.04.06.00.010.00.00.0
239Frappuccino® Blended CrèmeVanilla Bean (Without Whipped Cream)Whole Milk200.03.52.00.110.0160.039.00.038.03.06.00.010.00.00.0
240Frappuccino® Blended CrèmeVanilla Bean (Without Whipped Cream)Soymilk180.01.50.20.00.0160.037.01.035.03.04.00.010.06.00.0
241Frappuccino® Blended CrèmeVanilla Bean (Without Whipped Cream)Grande Nonfat Milk240.00.10.10.05.0230.056.00.055.05.08.00.015.00.00.0